WO2022038832A1 - Business support system and business support method - Google Patents

Business support system and business support method Download PDF

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Publication number
WO2022038832A1
WO2022038832A1 PCT/JP2021/015986 JP2021015986W WO2022038832A1 WO 2022038832 A1 WO2022038832 A1 WO 2022038832A1 JP 2021015986 W JP2021015986 W JP 2021015986W WO 2022038832 A1 WO2022038832 A1 WO 2022038832A1
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prediction
past
support system
prediction error
business support
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French (fr)
Japanese (ja)
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晃治 陰山
智裕 山本
秀之 田所
茂寿 崎村
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株式会社日立製作所
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

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  • the present invention relates to a business support system and a business support method that support a manager who formulates a business plan based on predicted values of machine learning.
  • Prediction systems that obtain predicted values using statistical prediction models such as neural networks, deep learning, and multiple regression analysis, which are machine learning, are being put to practical use in various fields.
  • These prediction models give training data in which the values of input items and output items are aligned in advance at the training stage, and adjust the values of the parameters in the prediction model to appropriate values so that the calculated values of the prediction model match as much as possible. .. After that, in the prediction stage, the value of the input item is given to this prediction model, and the prediction value is calculated.
  • the prediction model is identified so as to match the training data given to the learning stage as much as possible, but the prediction value is not always accurate. This becomes remarkable when the quality and quantity of training data are not sufficient. Therefore, it is difficult for the manager who formulates the business plan with reference to the predicted value to judge how much the predicted value can be trusted. In addition, even if it is possible to roughly estimate the prediction error of how much the predicted value deviates, it may not be possible to immediately formulate a business plan such as how to allocate resources accordingly.
  • Patent Document 1 As a background technology in this technical field, for example, there is a technology such as Patent Document 1.
  • the sewage inflow amount prediction device of Patent Document 1 includes a storage unit for storing predicted values and prediction errors, and a prediction step width determining unit as components. With this configuration, the time length from the current time to the future time when the inflow of sewage is to be predicted can be changed arbitrarily.
  • Patent Document 3 indicates that in a conventional prediction method using a regression model, a neural net, or the like, an error bar, a standard deviation, or the like is used to evaluate an error in a future predicted value. (Fig. 13 etc.)
  • Patent Document 1 does not describe the function of showing the user how much the prediction error was. No statistics on the prediction error are given, so the user does not know how likely the prediction is to be off.
  • Patent Document 2 is a frequency distribution of predicted values and not an error frequency distribution, the user does not know how likely the predicted values are to deviate.
  • an object of the present invention is to determine how much the manager who formulates a business plan can trust the predicted value in a business support system and a business support method that support the formulation of a business plan based on the predicted value of machine learning.
  • the purpose is to provide a business support system and a business support method that can make an accurate judgment and can quickly formulate a business plan.
  • the present invention is a business support system that supports the formulation of a business plan, in which the past predicted value and the past actual value, or the past predicted value and the past actual value are calculated.
  • a storage unit that stores prediction errors, a statistical analysis unit that statistically processes past prediction errors and obtains prediction error statistical processing results, and a prediction error statistical processing result obtained by the statistical analysis unit are added to or subtracted from future prediction values. It is characterized by including a prediction error reflection prediction value calculation unit for obtaining a prediction error reflection prediction value, and a display unit for displaying the prediction error reflection prediction value obtained by the prediction error reflection prediction value calculation unit.
  • the present invention is a business support method for supporting the formulation of a business plan, in which (a) a step of calculating a past prediction error from a past predicted value and a past actual value, and (b) the above (a). Prediction error by statistically processing the past prediction error calculated in step and obtaining the prediction error statistical processing result, and (c) adding or subtracting the prediction error statistical processing result obtained in step (b) to the future prediction value. It is characterized by having a step of obtaining a reflected predicted value, and (d) a step of displaying the predicted error reflected predicted value obtained in the step (c) above on the display unit.
  • a business support system and a business support method that support the formulation of a business plan based on the predicted value of machine learning, it is possible to accurately determine how much the manager who formulates the business plan can trust the predicted value. It is possible to realize a business support system and a business support method that can make a judgment and can quickly formulate a business plan.
  • Example 1 of this invention It is a functional block diagram of the business support system which concerns on Example 1 of this invention.
  • This is an example of a display unit that displays a histogram of the prediction error together with the predicted value. This is an example of displaying the frequency (frequency) of the prediction error in one day as a histogram. This is a display example showing the frequency distribution.
  • Example 2 of this invention This is an example of a display unit in which a figure (square) that can be freely moved up and down is superimposed and displayed.
  • This is an example of a display unit that displays an automatically calculated future business plan.
  • This is an example of a display unit that displays data that is affected by the automatically calculated future business plan.
  • the business to be supported by the business support system of the present invention specifically includes freight delivery business, plant and equipment operation business, etc., and the future business plan is determined with reference to the predicted values. If it is a business, it is not limited to these.
  • FIG. 1 is a diagram showing the overall configuration of the business support system of this embodiment.
  • the past prediction error 12 stored in the storage unit 10 is given to the statistical analysis unit 14.
  • the learning time storage unit 32 which stores the date and time data when learning is performed by a method such as machine learning in the past, gives the learning time 34 of the prediction model to the specific period setting unit 28.
  • the learning time 34 of the prediction model is displayed in the specific period setting unit 28, and a screen is displayed on which the administrator can input the specific period setting input value 30 with reference to it.
  • the input specific period setting input value 30 is given to the statistical analysis unit 14.
  • the statistical analysis unit 14 takes out the data of the time corresponding to the specific period setting input value 30 from the past prediction error 12 and statistically analyzes it.
  • the prediction error statistical processing result 16 obtained as a result is given to the prediction error reflection prediction value calculation unit 18.
  • the prediction error reflection prediction value calculation unit 18 is also given a future prediction value 20 predicted by the prediction unit 26.
  • the prediction unit 26 outputs a future prediction value from the reference time.
  • the prediction error reflection prediction value calculation unit 18 obtains the prediction error reflection prediction value 22 by adding or subtracting the prediction error statistical processing result 16 to the future prediction value 20, and gives it to the display unit 24.
  • the display unit 24 displays the prediction error reflection prediction value 22 on the screen. For example, display a graph in which one axis is time (days) and the other axis is a future prediction error reflection predicted value 22. As a result, the manager can know the predicted value including the statistical value of the past prediction error, and can formulate the business plan more accurately.
  • a future predicted value 20 is also given to the display unit 24, and a future predicted value 20 before addition / subtraction by the prediction error reflection predicted value calculation unit 18 is also displayed.
  • the manager can know how much the prediction error was, and can accurately judge how reliable the predicted value is.
  • the past prediction error 12 is given to the statistical analysis unit 14 from the storage unit 10 and used as it is, but the storage unit 10 may store the past predicted value and the past actual value, in which case.
  • the past prediction error 12 may be calculated based on the past predicted value and the past actual value as preprocessing, and the past prediction error 12 may be statistically analyzed.
  • the actual value 45 stored in the actual value storage unit 43 at the past time point is also given to the display unit 24.
  • the predicted value 36 stored in the predicted value storage unit 42 at the past time point is also given to the display unit 24.
  • the display unit 24 displays the actual value 45 at the past time point and the predicted value 36 at the past time point. This makes it possible to know with what accuracy the future predicted value 20 was correct in the past.
  • the predicted value 36 at the past time point may exist more than once depending on how far ahead is predicted even at the same past time point. For example, if there is a forecasting unit 26 that predicts the future forecast value 20 for three periods of 10 minutes, 20 minutes, and 30 minutes as the time length, at the stage of 12:00 yesterday, 12:10, 12:20, Three future forecasts of 20 at 12:30 have been obtained. At 12:10, 10 minutes later, three future forecast values of 20 at 12:20, 12:30, and 12:40 were obtained.
  • the target data time length setting unit 48 sets a fixed time length 50.
  • the time length 50 set by the target data time length setting unit 48 is given to the display unit 24. For example, if the time length 50 is set to 10 minutes, the display unit 24 displays the future predicted value 20 predicted 10 minutes later at 12:20 at 12:30 yesterday, and 20 at 12:10. Even if there is a future predicted value 20 that predicts minutes later or a future predicted value 20 that predicts 30 minutes later at 12:00, it is not displayed on the screen.
  • the prediction error statistical processing result 40 at the past time point stored in the prediction error statistical processing result storage unit 38 is given to the past time point prediction error reflection prediction value calculation unit 44.
  • the predicted value calculation unit 44 that reflects the past time point prediction error is also given the predicted value 36 at the past time point from the predicted value storage unit 42.
  • the past time point prediction error reflection prediction value calculation unit 44 obtains the past time point prediction error reflection prediction value 46 by adding or subtracting the past time point prediction error statistical processing result 40 to the past time point prediction value 36, and the display unit 24. Give to.
  • the display unit 24 displays the past time point prediction error reflection prediction value 46 on the screen. This makes it possible to know how the past time point prediction error reflection prediction value 46 at the past time point has changed.
  • the prediction error statistical processing result 40 at the past time point given to the past time point prediction error reflection prediction value calculation unit 44 is the actual value 45 and the predicted value storage at the past time point stored in the actual value storage unit 43. It may be the result of statistically processing the predicted value 36 stored in the part 42 at the past time point.
  • FIG. 2 is an example of the display screen of the display unit 24.
  • the future predicted value 20 is displayed by a dotted dotted line from the 0th day on the horizontal axis (number of days) to the 4th day.
  • Prediction error reflection The predicted value 22 is displayed as a histogram rotated 90 degrees to the left at the 1st, 2nd, 3rd, and 4th days on the horizontal axis (number of days).
  • the horizontal axis (number of days) is a negative value
  • the transition from the past to the present is shown as a broken line at the points from -4 days to 0 days
  • the solid line is the actual value 45 at the past time
  • the dotted line is. It is a predicted value 36 at a past time point.
  • the histogram displayed in Fig. 2 is obtained by the following procedure.
  • the specific period setting input value 30 is input by the specific period setting unit 28.
  • the period of the data to be analyzed is set.
  • the learning time 34 of the prediction model is displayed in the specific period setting unit 28.
  • the learning time 34 of the previous prediction model is 3 months ago
  • the learning time 34 of the previous prediction model is 1 year and 3 months ago
  • the learning time 34 of the previous prediction model is 2 years and 6 months ago. Is displayed.
  • the administrator refers to this information and inputs the data to be the target of the histogram from when to when in the specific period setting unit 28. For example, if the period is set from the past 2 months to 1 month ago, the period of 1 month after the learning time 34 of the previous prediction model can be set as the analysis target.
  • the statistical analysis unit 14 performs statistical analysis on the past prediction error 12 of the period of the specific period setting input value 30 set in (1). For example, for the histogram displayed at the position where the horizontal axis (number of days) of FIG. 2 is "1 day", the past prediction error 12 that predicts the future by only one day from the present as the time length is used. Table 1 shows an example of frequency distribution data with a past prediction error of 12.
  • FIG. 3 The results of displaying the data in Table 1 as a histogram are shown in FIG. Looking at FIG. 3, it can be seen that the minimum value of the data range of the prediction error of the number of cargoes is -50 [pieces / day] and the maximum value is -10 [pieces / day]. Since the prediction error is about -30 [pieces / day] on average and the frequency around it is high, the future prediction value 20 may deviate from the future prediction value 20 by about -30 [pieces / day] for only one day. Can be inferred to be high. Since the predicted value shown by the dotted line on the horizontal axis (number of days) in Fig.
  • the histogram in FIG. 3 was rotated 90 degrees to the left, and the value of the data section of the prediction error was placed at the position where the future prediction value 20 was added. It is a horizontal histogram inside.
  • the administrator can accumulate the learning data. It becomes possible to confirm how much the learning has progressed and the accuracy has improved.
  • the actual value 45 at the past time is shown by the solid line, and the dotted line is shown. It is a predicted value 36 at a past time point.
  • the predicted value 36 at the past time point is, for example, a value for which the future is predicted for only one day as the time length at each time point. This figure shows that the actual value 45 at the past time point from -4 days to 0 day was smaller than the predicted value 36 at the past time point.
  • the horizontal axis (days) in Fig. 2 is a negative value-4 days to 0 days, the horizontal axis (days) is the same as 1 to 4 days.
  • the past time prediction error reflection prediction value 46 may be superimposed and displayed on the horizontal histogram. This makes it possible to know how the past time point prediction error reflection prediction value 46 has changed with respect to the past time point prediction value 36.
  • the maximum value, the minimum value, the average value, the mode value, the median value, and the standard deviation are used as long as the past prediction error 12 is targeted. It may be the method you ask for. All of them are added to or subtracted from the future predicted value 20 and displayed in the figure of FIG. If it is the maximum value and the minimum value, it is displayed as an error bar. If it is the average value, mode value, or median value, it is displayed as one point or one short line.
  • the standard deviation the value obtained by subtracting the constant multiple of the standard deviation from the mean value and the value obtained by adding the constant multiple of the standard deviation to the mean value are displayed as error bars.
  • the histograms, error bars, single points, or single short lines may not be displayed separately, but may be displayed in duplicate at the same time.
  • a histogram, a color, a shade of color, a size of a width, a length of a length, a line graph, a curve, an envelope, etc. are displayed. May be.
  • the prediction unit 26 in FIG. 1 includes at least one of neural network, deep learning, and multiple regression calculation, but is not limited to these as long as it is a calculation method for machine learning using training data.
  • the business support system of this embodiment has a past predicted value (predicted value 36 at a past time point) and a past actual value (actual value 45 at a past time point), or a past predicted value.
  • the storage unit storage unit 10, predicted value storage unit 42, actual value storage unit 43
  • the storage unit stores the past prediction error 12 calculated from the past actual values and the past prediction error 12 are statistically processed, and the prediction error is predicted.
  • Prediction error reflection predicted value calculation unit that obtains the prediction error reflection prediction value 22 by adding / subtracting the prediction error statistical processing result 16 obtained by the statistical analysis unit 14 and the statistical analysis unit 14 that obtains the statistical processing result 16 to the future prediction value 20.
  • 18 and a display unit 24 for displaying the prediction error reflection prediction value 22 obtained by the prediction error reflection prediction value calculation unit 18.
  • a past prediction error 12 is obtained from a past predicted value (predicted value 36 at a past time point) and a past actual value (actual value 45 at a past time point).
  • the manager who formulates the business plan can easily guess how much the future forecast value will be after grasping the prediction error of the forecasting department based on the past performance, and a more appropriate business plan. It can be used for the formulation of.
  • the business plan can be formulated in a short time.
  • FIG. 5 is a diagram showing the overall configuration of the business support system of this embodiment.
  • the graphic position information 52, the business plan automatic calculation unit 54, the business plan 56, and the affected data 58 are added to the components shown in the first embodiment (FIG. 1). ing.
  • Other configurations are the same as those in the first embodiment (FIG. 1). Of these, the figure position information 52 will be described with reference to FIG.
  • Fig. 6 squares are displayed on the horizontal axis (number of days) at the 1st, 2nd, 3rd, and 4th days.
  • This figure shows the number of cargoes estimated by the administrator, and can be moved up and down on the display unit 24 on each day by operating the mouse, keyboard, touch panel, and the like.
  • the shape of the figure is not limited to a square, and may be a different shape.
  • the value of the vertical axis (number of cargoes) of the moved figure becomes the figure position information 52.
  • the figure position information 52 is given from the display unit 24 to the business plan automatic calculation unit 54.
  • the business plan automatic calculation unit 54 calculates the business plan 56 based on the graphic position information 52 and gives it to the display unit 24. Further, the data 58 affected by the business plan 56 is also given to the display unit 24 and displayed.
  • FIG. 7 shows an example of the business plan 56 displayed on the display unit 24.
  • the business plan 56 to be drafted is the number of trucks in operation in this example.
  • FIG. 7 shows how many trucks are required to deliver the cargo to the number of cargoes shown in FIG.
  • the horizontal axis (days) is -4 days to 0 days, and the past actual values including today are displayed, and the horizontal axis (days) is 1 to 4 days in the future business plan 56. Equivalent to.
  • FIGS. 6 and 7 are displayed simultaneously on the same screen, and the display of the business plan 56 of FIG. 7 may change in real time according to the operation of moving the figure (square) in FIG. 6 up and down. desirable.
  • the manager who formulates the business plan estimates the number of cargoes while checking the future predicted value 20 of the number of cargoes and the predicted error reflection predicted value 22 obtained by adding / subtracting the frequency distribution of the past predicted error to the predicted value in FIG. , It is easy to interactively grasp how many trucks will be operated if the estimated number changes.
  • the number of trucks in operation is small, and if the number of trucks in operation can be reduced by one by slightly reducing the estimated number, it is possible to easily formulate a plan under the condition that the estimated number is reduced. On the contrary, if the number of trucks operated increases by one by slightly increasing the estimated number, it is possible to formulate a plan under the condition that the estimated number is increased in consideration of the risk.
  • Figure 8 shows an example of displaying the data 58 that is affected by the automatically calculated future business plan.
  • data 58 affected by future business plans changes in the number of cargoes in stock in the warehouse are displayed in a line graph. Similar to FIG. 7, the horizontal axis (days) is -4 days to 0 days, and the past actual values including today of the inventories in the warehouse are displayed.
  • the data 58 on the horizontal axis (number of days) from 1 to 4 days is affected by the future business plan 56.
  • There are various methods for calculating the affected data 58 but a simple method is the result of subtracting the value obtained by multiplying the number of trucks in FIG. 7 by the transport capacity per truck from the number of cargoes in FIG. It can be calculated as the value obtained by adding the cargo in stock in the warehouse up to the time before that.
  • FIG. 8 is also displayed at the same time on the same screen as FIG. 6, and the display of the data 58 affected by FIG. 8 changes in real time according to the operation of moving the figure (square) of FIG. 6 up and down. Is desirable.
  • the trend graph of the affected data 58 for each item should be displayed on the same screen at the same time.
  • the warehouse inventory cargo is displayed, but the manager who formulates the business plan can see the change in the number of warehouse inventory cargo while moving the figure (square) in FIG. 6 up and down.
  • it is possible to formulate a plan for the number of trucks in operation so that the number of cargo in stock in the warehouse will be at an appropriate level.
  • the data affected by the automatically calculated business plan is also displayed. Therefore, in addition to the effect of the first embodiment, the displayed business plan and the affected data are displayed. You can adjust the estimated value while checking.
  • the present invention is not limited to the above-described embodiment, but includes various modifications.
  • the above embodiments have been described in detail to aid in understanding of the present invention and are not necessarily limited to those comprising all of the described configurations.
  • it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment and it is also possible to add the configuration of another embodiment to the configuration of one embodiment.

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Abstract

Provided are a business support system and a business support method which support formulation of a business plan based on predicted values from machine learning, and enable a manager who formulates a business plan to accurately determine how reliable the predicted values are, and quickly draft the business plan. The business support system which supports formulation of a business plan is characterized by comprising: a storage unit that stores past predicted values and past actual values, or past prediction errors calculated from past predicted values and past actual values; a statistical analysis unit that performs statistical processing on the past prediction errors, and obtains a result of the statistical processing on the prediction errors; a predicted value reflecting prediction error calculation unit that calculates a predicted value reflecting prediction error by adding or subtracting the result of the statistical processing on the prediction errors obtained in the statistical analysis unit, to or from a future predicted value; and a display unit that displays the predicted value reflecting prediction error calculated in the predicted value reflecting prediction error calculation unit.

Description

業務支援システム、業務支援方法Business support system, business support method
　本発明は、機械学習の予測値に基づき業務計画を策定する管理者を支援する業務支援システム及び業務支援方法に関する。 The present invention relates to a business support system and a business support method that support a manager who formulates a business plan based on predicted values of machine learning.
　機械学習であるニューラルネットワークやディープラーニング、重回帰分析など統計的な予測モデルを用いて予測値を求める予測システムが、様々な分野において実用化されつつある。これらの予測モデルは、あらかじめ学習段階に入力項目と出力項目の値が揃った学習データを与え、予測モデルの計算値ができるだけ合致するように予測モデル内のパラメータの値を適切な値に調節する。その後、予測段階ではこの予測モデルに入力項目の値を与え、予測値を計算する。 Prediction systems that obtain predicted values using statistical prediction models such as neural networks, deep learning, and multiple regression analysis, which are machine learning, are being put to practical use in various fields. These prediction models give training data in which the values of input items and output items are aligned in advance at the training stage, and adjust the values of the parameters in the prediction model to appropriate values so that the calculated values of the prediction model match as much as possible. .. After that, in the prediction stage, the value of the input item is given to this prediction model, and the prediction value is calculated.
　予測モデルは学習段階に与えた学習データとできるだけ合致するように同定されるが、予測値は必ずしも正確ではない。これは、学習データの質や量が十分でない場合などには顕著となる。したがって、予測値を参考にして業務計画を策定する管理者は、予測値をどの程度信頼して良いかを判断することが困難である。また、予測値がどの程度ずれるか予測誤差をおおよそ推測できる場合であっても、それに応じてリソースをどのように配分するかなど業務計画をすぐには立案できない場合もある。 The prediction model is identified so as to match the training data given to the learning stage as much as possible, but the prediction value is not always accurate. This becomes remarkable when the quality and quantity of training data are not sufficient. Therefore, it is difficult for the manager who formulates the business plan with reference to the predicted value to judge how much the predicted value can be trusted. In addition, even if it is possible to roughly estimate the prediction error of how much the predicted value deviates, it may not be possible to immediately formulate a business plan such as how to allocate resources accordingly.
　本技術分野の背景技術として、例えば、特許文献１のような技術がある。特許文献１の下水流入量予測装置では、構成要素として、予測値と予測誤差を記憶する記憶部と、予測ステップ幅決定部とを備える。この構成により、現在の時刻から下水流入量を予測しようとする未来の時刻までの時間長を任意に変化させられるとしている。 As a background technology in this technical field, for example, there is a technology such as Patent Document 1. The sewage inflow amount prediction device of Patent Document 1 includes a storage unit for storing predicted values and prediction errors, and a prediction step width determining unit as components. With this configuration, the time length from the current time to the future time when the inflow of sewage is to be predicted can be changed arbitrarily.
　また、特許文献２の電力予測方法、装置、及びプログラムでは、地点毎の電力データの多数組の乱数データあるいは一地点の時間帯毎の電力データや2時間帯間の相関係数値を再現する多数組の乱数データを発生させ、それらに基づく予測値を度数分布として纏めることが記されている。これにより、電力系統の運用者は、地域の合計発電出力の予測される範囲と確度とを得ることができるとしている。 Further, in the power prediction method, device, and program of Patent Document 2, a large number of random number sets of power data for each point, power data for each time zone at one point, and a large number for reproducing correlation coefficient values between two time zones are reproduced. It is described that a set of random number data is generated and the predicted values based on them are summarized as a frequency distribution. As a result, the operator of the power system can obtain the predicted range and accuracy of the total power generation output of the region.
　また、特許文献３には、回帰モデルやニューラルネットなどを用いた従来の予測方法において、将来の予測値の誤差を評価するために、エラーバーや標準偏差などを用いることが示されている。（図１３等） Further, Patent Document 3 indicates that in a conventional prediction method using a regression model, a neural net, or the like, an error bar, a standard deviation, or the like is used to evaluate an error in a future predicted value. (Fig. 13 etc.)
特開２０１６－２１１８７７号公報Japanese Unexamined Patent Publication No. 2016-21187 特開２０１４－５４０４８号公報Japanese Unexamined Patent Publication No. 2014-54048 特開２０１０－２０４４２号公報Japanese Unexamined Patent Publication No. 2010-20442
　しかしながら、上記特許文献１には予測誤差がどの程度であったかをユーザーへ示す機能に関して記載されていない。予測誤差の統計値も示されないため、ユーザーは予測値がどの程度外れる可能性が高いか分からない。 However, the above-mentioned Patent Document 1 does not describe the function of showing the user how much the prediction error was. No statistics on the prediction error are given, so the user does not know how likely the prediction is to be off.
　また、上記特許文献２は予測値の度数分布であり、誤差の度数分布ではないため、ユーザーは予測値がどの程度外れる可能性が高いかが分からない。 Further, since the above-mentioned Patent Document 2 is a frequency distribution of predicted values and not an error frequency distribution, the user does not know how likely the predicted values are to deviate.
　また、上記特許文献３のように最大最小や３σを示すエラーバーを用いた場合でも、予測誤差が偏った分布となる可能性があり、ユーザーは予測値がどの程度外れるのか正確に把握することができない。 Further, even when an error bar indicating the maximum / minimum or 3σ is used as in Patent Document 3, the prediction error may be unevenly distributed, and the user must accurately grasp how much the predicted value deviates. I can't.
　したがって、これらの特許文献の技術を用いても、業務計画を策定する管理者は予測値をどの程度信頼して良いか判断できない。まして、リソースを今後どのように配分するかなど業務計画を予測値に基づいてすぐに立案できない。 Therefore, even if the technology of these patent documents is used, the manager who formulates the business plan cannot judge how much the predicted value can be trusted. Moreover, it is not possible to immediately formulate a business plan based on predicted values, such as how to allocate resources in the future.
　そこで、本発明の目的は、機械学習の予測値に基づく業務計画の策定を支援する業務支援システム及び業務支援方法において、業務計画を策定する管理者が予測値をどの程度信頼して良いかを正確に判断することができ、なおかつ、迅速な業務計画の立案が可能な業務支援システム及び業務支援方法を提供することにある。 Therefore, an object of the present invention is to determine how much the manager who formulates a business plan can trust the predicted value in a business support system and a business support method that support the formulation of a business plan based on the predicted value of machine learning. The purpose is to provide a business support system and a business support method that can make an accurate judgment and can quickly formulate a business plan.
　上記課題を解決するために、本発明は、業務計画の策定を支援する業務支援システムにおいて、過去の予測値と過去の実績値、或いは過去の予測値と過去の実績値から算出された過去の予測誤差を記憶する記憶部と、過去の予測誤差を統計処理し、予測誤差統計処理結果を求める統計解析部と、前記統計解析部で求めた予測誤差統計処理結果を将来の予測値に加減算して予測誤差反映予測値を求める予測誤差反映予測値計算部と、前記予測誤差反映予測値計算部で求めた予測誤差反映予測値を表示する表示部と、を備えることを特徴とする。 In order to solve the above problems, the present invention is a business support system that supports the formulation of a business plan, in which the past predicted value and the past actual value, or the past predicted value and the past actual value are calculated. A storage unit that stores prediction errors, a statistical analysis unit that statistically processes past prediction errors and obtains prediction error statistical processing results, and a prediction error statistical processing result obtained by the statistical analysis unit are added to or subtracted from future prediction values. It is characterized by including a prediction error reflection prediction value calculation unit for obtaining a prediction error reflection prediction value, and a display unit for displaying the prediction error reflection prediction value obtained by the prediction error reflection prediction value calculation unit.
　また、本発明は、業務計画の策定を支援する業務支援方法であって、（ａ）過去の予測値と過去の実績値から過去の予測誤差を算出するステップと、（ｂ）前記（ａ）ステップで算出した過去の予測誤差を統計処理し、予測誤差統計処理結果を求めるステップと、（ｃ）前記（ｂ）ステップで求めた予測誤差統計処理結果を将来の予測値に加減算して予測誤差反映予測値を求めるステップと、（ｄ）前記（ｃ）ステップで求めた予測誤差反映予測値を表示部に表示するステップと、を有することを特徴とする。 Further, the present invention is a business support method for supporting the formulation of a business plan, in which (a) a step of calculating a past prediction error from a past predicted value and a past actual value, and (b) the above (a). Prediction error by statistically processing the past prediction error calculated in step and obtaining the prediction error statistical processing result, and (c) adding or subtracting the prediction error statistical processing result obtained in step (b) to the future prediction value. It is characterized by having a step of obtaining a reflected predicted value, and (d) a step of displaying the predicted error reflected predicted value obtained in the step (c) above on the display unit.
　本発明によれば、機械学習の予測値に基づく業務計画の策定を支援する業務支援システム及び業務支援方法において、業務計画を策定する管理者が予測値をどの程度信頼して良いかを正確に判断することができ、なおかつ、迅速な業務計画の立案が可能な業務支援システム及び業務支援方法を実現することができる。 According to the present invention, in a business support system and a business support method that support the formulation of a business plan based on the predicted value of machine learning, it is possible to accurately determine how much the manager who formulates the business plan can trust the predicted value. It is possible to realize a business support system and a business support method that can make a judgment and can quickly formulate a business plan.
　上記した以外の課題、構成及び効果は、以下の実施形態の説明により明らかにされる。 Issues, configurations and effects other than those described above will be clarified by the explanation of the following embodiments.
本発明の実施例１に係る業務支援システムの機能ブロック図である。It is a functional block diagram of the business support system which concerns on Example 1 of this invention. 予測誤差のヒストグラムを予測値とともに表示した表示部の例である。This is an example of a display unit that displays a histogram of the prediction error together with the predicted value. １日における予測誤差の度数（頻度）をヒストグラムで表示した例である。This is an example of displaying the frequency (frequency) of the prediction error in one day as a histogram. 度数分布を示す表示例である。This is a display example showing the frequency distribution. 本発明の実施例２に係る業務支援システムの機能ブロック図である。It is a functional block diagram of the business support system which concerns on Example 2 of this invention. 上下へ自由に移動できる図形（正方形）を重畳表示した表示部の例である。This is an example of a display unit in which a figure (square) that can be freely moved up and down is superimposed and displayed. 自動計算された将来の業務計画を表示した表示部の例である。This is an example of a display unit that displays an automatically calculated future business plan. 自動計算された将来の業務計画により影響を受けるデータを表示した表示部の例である。This is an example of a display unit that displays data that is affected by the automatically calculated future business plan.
　以下、図面を用いて本発明の実施例を説明する。各図面において同一の構成については同一の符号を付し、重複する部分についてはその詳細な説明は省略する。 Hereinafter, embodiments of the present invention will be described with reference to the drawings. In each drawing, the same components are designated by the same reference numerals, and detailed description of overlapping portions will be omitted.
　なお、本発明の業務支援システムが支援する対象の業務は、具体的には貨物の配送業務、プラントや装置の運転業務などが挙げられるが、予測値を参考にして将来の業務計画を決定する業務であればこれらに限定されない。 The business to be supported by the business support system of the present invention specifically includes freight delivery business, plant and equipment operation business, etc., and the future business plan is determined with reference to the predicted values. If it is a business, it is not limited to these.
　図１から図４を参照して、本発明の実施例１に係る業務支援システム及び業務支援方法について説明する。 The business support system and the business support method according to the first embodiment of the present invention will be described with reference to FIGS. 1 to 4.
　図１は、本実施例の業務支援システムの全体構成を示す図である。先ず、記憶部１０に記憶されていた過去の予測誤差１２は統計解析部１４に与えられる。また、過去に機械学習などの手法で学習を実施した際の日時データを記憶している学習時期記憶部３２からは、予測モデルの学習時期３４が特定期間設定部２８に与えられる。 FIG. 1 is a diagram showing the overall configuration of the business support system of this embodiment. First, the past prediction error 12 stored in the storage unit 10 is given to the statistical analysis unit 14. Further, the learning time storage unit 32, which stores the date and time data when learning is performed by a method such as machine learning in the past, gives the learning time 34 of the prediction model to the specific period setting unit 28.
　特定期間設定部２８には予測モデルの学習時期３４が表示されるとともに、それを参考にして管理者が特定期間設定入力値３０を入力できる画面が表示される。入力した特定期間設定入力値３０は統計解析部１４に与えられる。 The learning time 34 of the prediction model is displayed in the specific period setting unit 28, and a screen is displayed on which the administrator can input the specific period setting input value 30 with reference to it. The input specific period setting input value 30 is given to the statistical analysis unit 14.
　統計解析部１４は、特定期間設定入力値３０に対応する時期のデータを過去の予測誤差１２の中から取り出して統計解析する。その結果として得られた予測誤差統計処理結果１６は予測誤差反映予測値計算部１８に与えられる。予測誤差反映予測値計算部１８には、予測部２６で予測された将来の予測値２０も与えられる。予測部２６は基準となる時刻より将来の予測値を出力する。 The statistical analysis unit 14 takes out the data of the time corresponding to the specific period setting input value 30 from the past prediction error 12 and statistically analyzes it. The prediction error statistical processing result 16 obtained as a result is given to the prediction error reflection prediction value calculation unit 18. The prediction error reflection prediction value calculation unit 18 is also given a future prediction value 20 predicted by the prediction unit 26. The prediction unit 26 outputs a future prediction value from the reference time.
　予測誤差反映予測値計算部１８では、将来の予測値２０に予測誤差統計処理結果１６を加減算して予測誤差反映予測値２２を求め、表示部２４に与える。表示部２４では、予測誤差反映予測値２２を画面に表示する。例えば、一方の軸が時間（日数）であり、他方の軸が将来の予測誤差反映予測値２２であるグラフを表示する。これにより、管理者は過去の予測誤差の統計値を加味した予測値を知ることができ、業務計画をより的確に策定することができる。 The prediction error reflection prediction value calculation unit 18 obtains the prediction error reflection prediction value 22 by adding or subtracting the prediction error statistical processing result 16 to the future prediction value 20, and gives it to the display unit 24. The display unit 24 displays the prediction error reflection prediction value 22 on the screen. For example, display a graph in which one axis is time (days) and the other axis is a future prediction error reflection predicted value 22. As a result, the manager can know the predicted value including the statistical value of the past prediction error, and can formulate the business plan more accurately.
　また、表示部２４には将来の予測値２０も与えられ、予測誤差反映予測値計算部１８で加減算される前の将来の予測値２０も表示する。これにより、管理者は予測誤差がどの程度であったかを知ることができ、予測値をどの程度信頼して良いかを正確に判断することができる。 Further, a future predicted value 20 is also given to the display unit 24, and a future predicted value 20 before addition / subtraction by the prediction error reflection predicted value calculation unit 18 is also displayed. As a result, the manager can know how much the prediction error was, and can accurately judge how reliable the predicted value is.
　上述の例では記憶部１０から過去の予測誤差１２を統計解析部１４に与えてそのまま用いたが、記憶部１０には過去の予測値と過去の実績値が記憶されていても良く、その場合には統計解析部１４では前処理として過去の予測誤差１２を過去の予測値と過去の実績値に基づいて計算し、それを統計解析することでも良い。 In the above example, the past prediction error 12 is given to the statistical analysis unit 14 from the storage unit 10 and used as it is, but the storage unit 10 may store the past predicted value and the past actual value, in which case. In the statistical analysis unit 14, the past prediction error 12 may be calculated based on the past predicted value and the past actual value as preprocessing, and the past prediction error 12 may be statistically analyzed.
　実績値記憶部４３に記憶されている過去の時点での実績値４５も表示部２４に与えられる。また、予測値記憶部４２に記憶されている過去の時点での予測値３６も表示部２４に与えられる。表示部２４には過去の時点での実績値４５と過去の時点での予測値３６を表示する。これにより、これまで過去にどの程度の精度で将来の予測値２０が正しかったかを知ることができる。 The actual value 45 stored in the actual value storage unit 43 at the past time point is also given to the display unit 24. Further, the predicted value 36 stored in the predicted value storage unit 42 at the past time point is also given to the display unit 24. The display unit 24 displays the actual value 45 at the past time point and the predicted value 36 at the past time point. This makes it possible to know with what accuracy the future predicted value 20 was correct in the past.
　過去の時点での予測値３６は、同じ過去の時点であってもどの程度先を予測したかによって複数存在する場合がある。例えば、時間長として10分、20分、30分の3期間の将来の予測値２０を予測する予測部２６がある場合、昨日12時00分の段階では12時10分、12時20分、12時30分の3つの将来の予測値２０が得られたことになる。その10分後の12時10分には、12時20分、12時30分、12時40分の3つの将来の予測値２０が得られたことになる。 The predicted value 36 at the past time point may exist more than once depending on how far ahead is predicted even at the same past time point. For example, if there is a forecasting unit 26 that predicts the future forecast value 20 for three periods of 10 minutes, 20 minutes, and 30 minutes as the time length, at the stage of 12:00 yesterday, 12:10, 12:20, Three future forecasts of 20 at 12:30 have been obtained. At 12:10, 10 minutes later, three future forecast values of 20 at 12:20, 12:30, and 12:40 were obtained.
　これらの将来の予測値２０をすべて表示部２４に表示すると画面が煩雑となるため、一部の情報のみに絞って表示するのも良い。これにより、情報の把握が容易になると同時に、勘違いや見誤り発生のリスクを低減できる。情報の絞り方の一つとして以下に時間長を一定とする方法について述べるが、絞り方はこれのみに限定されない。 If all of these future predicted values 20 are displayed on the display unit 24, the screen becomes complicated, so it is good to display only a part of the information. This makes it easier to grasp the information and at the same time reduces the risk of misunderstandings and misunderstandings. The method of fixing the time length is described below as one of the methods of narrowing down the information, but the method of narrowing down is not limited to this.
　一定の時間長５０を設定するのが、対象データ時間長設定部４８である。対象データ時間長設定部４８で設定された時間長５０は表示部２４に与えられる。例えば時間長５０を10分と設定すると、表示部２４で昨日12時30分の箇所には12時20分に10分後を予測した将来の予測値２０を表示し、12時10分に20分後を予測した将来の予測値２０や12時00分に30分後を予測した将来の予測値２０があっても画面には表示しない。 The target data time length setting unit 48 sets a fixed time length 50. The time length 50 set by the target data time length setting unit 48 is given to the display unit 24. For example, if the time length 50 is set to 10 minutes, the display unit 24 displays the future predicted value 20 predicted 10 minutes later at 12:20 at 12:30 yesterday, and 20 at 12:10. Even if there is a future predicted value 20 that predicts minutes later or a future predicted value 20 that predicts 30 minutes later at 12:00, it is not displayed on the screen.
　予測誤差統計処理結果記憶部３８が記憶している過去の時点での予測誤差統計処理結果４０は過去時点予測誤差反映予測値計算部４４に与えられる。過去時点予測誤差反映予測値計算部４４には予測値記憶部４２から過去の時点での予測値３６も与えられる。過去時点予測誤差反映予測値計算部４４では過去の時点での予測値３６に過去の時点での予測誤差統計処理結果４０を加減算することによって過去時点予測誤差反映予測値４６を求め、表示部２４に与える。表示部２４では、この過去時点予測誤差反映予測値４６を画面上に表示する。これにより、過去の時点での過去時点予測誤差反映予測値４６がどのように推移したかを知ることができる。 The prediction error statistical processing result 40 at the past time point stored in the prediction error statistical processing result storage unit 38 is given to the past time point prediction error reflection prediction value calculation unit 44. The predicted value calculation unit 44 that reflects the past time point prediction error is also given the predicted value 36 at the past time point from the predicted value storage unit 42. The past time point prediction error reflection prediction value calculation unit 44 obtains the past time point prediction error reflection prediction value 46 by adding or subtracting the past time point prediction error statistical processing result 40 to the past time point prediction value 36, and the display unit 24. Give to. The display unit 24 displays the past time point prediction error reflection prediction value 46 on the screen. This makes it possible to know how the past time point prediction error reflection prediction value 46 at the past time point has changed.
　ここで、過去時点予測誤差反映予測値計算部４４に与えられる過去の時点での予測誤差統計処理結果４０は実績値記憶部４３に記憶されている過去の時点での実績値４５と予測値記憶部４２に記憶されている過去の時点での予測値３６を統計処理した結果でも良い。 Here, the prediction error statistical processing result 40 at the past time point given to the past time point prediction error reflection prediction value calculation unit 44 is the actual value 45 and the predicted value storage at the past time point stored in the actual value storage unit 43. It may be the result of statistically processing the predicted value 36 stored in the part 42 at the past time point.
　図２は表示部２４の表示画面の一例である。図中上部の下向きの黒塗り三角形が横軸（日数）＝0日を示しており、ここが現在の時点を示している。横軸（日数）の0日以降、4日にかけて点線の折れ線で将来の予測値２０が表示されている。予測誤差反映予測値２２は横軸（日数）の1日、2日、3日、4日の箇所に90度だけ左回転したヒストグラムで表示されている。一方、横軸（日数）が負の値である-4日から0日までの箇所には過去から現在の推移が折れ線で示されており、実線が過去の時点での実績値４５、点線が過去の時点での予測値３６である。 FIG. 2 is an example of the display screen of the display unit 24. The downward black triangle in the upper part of the figure indicates the horizontal axis (days) = 0 days, which indicates the current time point. The future predicted value 20 is displayed by a dotted dotted line from the 0th day on the horizontal axis (number of days) to the 4th day. Prediction error reflection The predicted value 22 is displayed as a histogram rotated 90 degrees to the left at the 1st, 2nd, 3rd, and 4th days on the horizontal axis (number of days). On the other hand, where the horizontal axis (number of days) is a negative value, the transition from the past to the present is shown as a broken line at the points from -4 days to 0 days, the solid line is the actual value 45 at the past time, and the dotted line is. It is a predicted value 36 at a past time point.
　この図２においては、横軸（日数）の1日、2日、3日、4日の箇所に表示されたヒストグラムは多くの点が将来の予測値２０を示す点線よりも下にある。これは、これまでの過去に得られた実績値が予測モデル（予測部２６）で求めた将来の予測値２０に比べて低かったことを意味している。 In FIG. 2, many points are below the dotted line showing the future predicted value 20 in the histograms displayed at the 1st, 2nd, 3rd, and 4th days on the horizontal axis (number of days). This means that the actual values obtained in the past so far were lower than the future predicted values 20 obtained by the prediction model (prediction unit 26).
　図２中に表示されているヒストグラムは、次の手順で求める。 The histogram displayed in Fig. 2 is obtained by the following procedure.
　（１）先ず、ヒストグラムとして描く統計処理の対象となるデータを決める。そのために、特定期間設定部２８で特定期間設定入力値３０を入力する。ここで解析対象とするデータの期間を設定する。特定期間設定部２８には予測モデルの学習時期３４が表示される。例えば、前回の予測モデルの学習時期３４は3か月前、前々回の予測モデルの学習時期３４は1年3か月前、さらにその前の予測モデルの学習時期３４は2年6か月前、のように表示される。 (1) First, determine the data to be subject to statistical processing to be drawn as a histogram. Therefore, the specific period setting input value 30 is input by the specific period setting unit 28. Here, the period of the data to be analyzed is set. The learning time 34 of the prediction model is displayed in the specific period setting unit 28. For example, the learning time 34 of the previous prediction model is 3 months ago, the learning time 34 of the previous prediction model is 1 year and 3 months ago, and the learning time 34 of the previous prediction model is 2 years and 6 months ago. Is displayed.
　管理者はこの情報を参考にして、ヒストグラムの対象となるデータをいつからいつまでのデータとするか特定期間設定部２８で入力する。例えば、過去2か月前から1か月前のように期間を設定すると、前回の予測モデルの学習時期３４より後の1か月の期間を解析対象と設定できる。 The administrator refers to this information and inputs the data to be the target of the histogram from when to when in the specific period setting unit 28. For example, if the period is set from the past 2 months to 1 month ago, the period of 1 month after the learning time 34 of the previous prediction model can be set as the analysis target.
　（２）統計解析部１４では、（１）で設定した特定期間設定入力値３０の期間の過去の予測誤差１２に対して統計解析を実施する。例えば図２の横軸（日数）が「1日」の箇所に表示されるヒストグラムは、現在から時間長として1日だけ将来を予測した過去の予測誤差１２を用いる。過去の予測誤差１２の度数分布データの例を表１に示す。 (2) The statistical analysis unit 14 performs statistical analysis on the past prediction error 12 of the period of the specific period setting input value 30 set in (1). For example, for the histogram displayed at the position where the horizontal axis (number of days) of FIG. 2 is "1 day", the past prediction error 12 that predicts the future by only one day from the present as the time length is used. Table 1 shows an example of frequency distribution data with a past prediction error of 12.
Figure JPOXMLDOC01-appb-T000001
 　また、表１のデータをヒストグラムとして表示した結果を図３に示す。図３を見ると、貨物数の予測誤差のデータ範囲の最小値は-50[個/日]、最大値は-10[個/日]であることが分かる。予測誤差は平均で-30[個/日]程度でありその周辺の度数が高いため、1日だけ将来の予測値２０も-30[個/日]程度だけ将来の予測値２０から外れる可能性が高いと推測できる。図２の横軸（日数）が1日の箇所で点線により示された予測値は235[個/日]であるため、実績値は235-30=205[個/日]程度となる可能性が高いと推測できる。このように予測値と図３があれば、将来の予測値２０が実際にはどの程度の値となる可能性が高いかを把握することができる。
Figure JPOXMLDOC01-appb-T000001
 The results of displaying the data in Table 1 as a histogram are shown in FIG. Looking at FIG. 3, it can be seen that the minimum value of the data range of the prediction error of the number of cargoes is -50 [pieces / day] and the maximum value is -10 [pieces / day]. Since the prediction error is about -30 [pieces / day] on average and the frequency around it is high, the future prediction value 20 may deviate from the future prediction value 20 by about -30 [pieces / day] for only one day. Can be inferred to be high. Since the predicted value shown by the dotted line on the horizontal axis (number of days) in Fig. 2 is 235 [pieces / day], the actual value may be about 235-30 = 205 [pieces / day]. Can be inferred to be high. With the predicted value and FIG. 3 in this way, it is possible to grasp how likely the future predicted value 20 is actually.
　別々の図ではなくこれを一目で把握できるよう、図３のヒストグラムを90度左方向に回転させ、予測誤差のデータ区間の値を将来の予測値２０を加えた位置へ配置したのが図２中の横向きのヒストグラムである。 In order to grasp this at a glance instead of separate figures, the histogram in FIG. 3 was rotated 90 degrees to the left, and the value of the data section of the prediction error was placed at the position where the future prediction value 20 was added. It is a horizontal histogram inside.
　図３のヒストグラムで最も度数が高い-25[個/日]～-35[個/日]の部分は、図２の中では縦軸（貨物数）が210（＝235-25）[個/日]～200（＝235-35）[個/日]の位置に表示される。同様に、度数が低い-10[個/日]～-15[個/日]の部分は、図２の縦軸（貨物数）が225（＝235-10）[個/日]～220（＝235-15）[個/日]の位置に表示される。 In the histogram of Fig. 3, the part with the highest frequency of -25 [pieces / day] to -35 [pieces / day] has a vertical axis (number of cargoes) of 210 (= 235-25) [pieces / day] in Fig. 2. It is displayed at the position of [day] to 200 (= 235-35) [pieces / day]. Similarly, in the part where the frequency is low -10 [pieces / day] to -15 [pieces / day], the vertical axis (number of cargoes) in Fig. 2 is 225 (= 235-10) [pieces / day] to 220 ( = 235-15) Displayed at the [piece / day] position.
　このように90度左方向に回転させたヒストグラムを表示することで、横軸（日数）が1日の箇所では貨物数が205（＝235-30）[個/日]程度となる可能性が高いことを一目で把握することができる。 By displaying the histogram rotated 90 degrees to the left in this way, there is a possibility that the number of cargoes will be about 205 (= 235-30) [pieces / day] where the horizontal axis (days) is one day. You can see at a glance what is expensive.
　予測誤差のヒストグラムは必ずしも図３で示したような左右対称にはならず、例えば横軸（日数）＝4日の箇所で示されるように、度数の高いデータ区間が中央ではなく一方に偏っている場合がある。特許文献３のように、単にエラーバーで予測誤差を表示するとこのような偏りを全く認識ができず、判断を誤る可能性がある。そこで、図２に示すように、ヒストグラムで表示することにより、過去に度数の高かった値が明確となり、より適切な判断が可能となる。 The histogram of the prediction error is not always symmetrical as shown in FIG. 3, and the data section with high frequency is biased to one side instead of the center, for example, as shown at the horizontal axis (days) = 4 days. There may be. If the prediction error is simply displayed by the error bar as in Patent Document 3, such a bias cannot be recognized at all, and there is a possibility that the judgment is erroneous. Therefore, as shown in FIG. 2, by displaying the histogram, the value having a high frequency in the past becomes clear, and a more appropriate judgment can be made.
　不十分な量や質の学習データで学習した予測モデルは一般的に精度が低いため、図２で示したヒストグラムの縦方向（予測誤差）の幅は大きく、将来の予測値２０からのズレも大きい。時間の経過とともにデータが徐々に増え、それも含めた学習データで再学習を繰り返すと精度が上がり、図２に横向きで示したヒストグラムの幅は小さくなり、将来の予測値２０からのズレも小さくなる。 Since the prediction model trained with training data of insufficient quantity and quality is generally inaccurate, the width of the histogram shown in FIG. 2 in the vertical direction (prediction error) is large, and the deviation from the future prediction value 20 is also large. big. The data gradually increases with the passage of time, and if re-learning is repeated with the training data including it, the accuracy improves, the width of the histogram shown horizontally in FIG. 2 becomes smaller, and the deviation from the future predicted value 20 becomes smaller. Become.
　特定期間設定入力値３０を前回の学習後、前々回の学習後、前々回より前の学習後、のように切り替えてヒストグラムの形状やその位置を確認することで、管理者は学習データが蓄積されて学習が進み、どの程度精度が向上しているかを確認することが可能となる。 By switching the specific period setting input value 30 after the previous learning, after the previous learning, and after the learning before the previous two times to check the shape and position of the histogram, the administrator can accumulate the learning data. It becomes possible to confirm how much the learning has progressed and the accuracy has improved.
　図２の横軸（日数）が負の値である-4日から0日までの期間において、実線で示されているのが過去の時点での実績値４５、点線で示されているのが過去の時点での予測値３６である。過去の時点での予測値３６は、例えばそれぞれの時点において時間長として1日だけ将来を予測した値である。この図では、-4日から0日まで過去の時点での実績値４５が過去の時点での予測値３６よりも小さかったことを示している。 In the period from -4 days to 0 days when the horizontal axis (number of days) in Fig. 2 is a negative value, the actual value 45 at the past time is shown by the solid line, and the dotted line is shown. It is a predicted value 36 at a past time point. The predicted value 36 at the past time point is, for example, a value for which the future is predicted for only one day as the time length at each time point. This figure shows that the actual value 45 at the past time point from -4 days to 0 day was smaller than the predicted value 36 at the past time point.
　これら実線と点線で示される折れ線に加え、図２の横軸（日数）が負の値である-4日から0日までに関しては、横軸（日数）が1日から4日までと同様に横向きのヒストグラムで過去時点予測誤差反映予測値４６を重畳して表示しても良い。これにより、過去の時点での予測値３６に対し、過去の時点での過去時点予測誤差反映予測値４６がどのように推移したかを知ることができる。 In addition to these solid and dotted lines, the horizontal axis (days) in Fig. 2 is a negative value-4 days to 0 days, the horizontal axis (days) is the same as 1 to 4 days. The past time prediction error reflection prediction value 46 may be superimposed and displayed on the horizontal histogram. This makes it possible to know how the past time point prediction error reflection prediction value 46 has changed with respect to the past time point prediction value 36.
　統計解析部１４での統計解析としては、上述したヒストグラム以外にも、過去の予測誤差１２を対象とするものであれば最大値、最小値、平均値、最頻値、中央値、標準偏差を求める方法でも良い。いずれについても、将来の予測値２０に加減算して図２の図中で表示する。最大値と最小値であれば、エラーバーとして表示する。平均値や最頻値、中央値であれば1点あるいは短い1本の線として表示する。標準偏差を使う場合には、平均値から標準偏差の定数倍を減算した値及び平均値に標準偏差の定数倍を加算した値をエラーバーとして表示する。これらのヒストグラム、エラーバー、1点あるいは短い1本の線の表示はいずれも別々にではなく、重複して同時に表示しても良い。 In the statistical analysis by the statistical analysis unit 14, in addition to the above-mentioned histogram, the maximum value, the minimum value, the average value, the mode value, the median value, and the standard deviation are used as long as the past prediction error 12 is targeted. It may be the method you ask for. All of them are added to or subtracted from the future predicted value 20 and displayed in the figure of FIG. If it is the maximum value and the minimum value, it is displayed as an error bar. If it is the average value, mode value, or median value, it is displayed as one point or one short line. When the standard deviation is used, the value obtained by subtracting the constant multiple of the standard deviation from the mean value and the value obtained by adding the constant multiple of the standard deviation to the mean value are displayed as error bars. The histograms, error bars, single points, or single short lines may not be displayed separately, but may be displayed in duplicate at the same time.
　表示部２４で表示する度数分布を示す図としては、図４に示すように、例えばヒストグラム、色、色の濃淡、幅の大小、長さの長短、折れ線グラフ、曲線、包絡線などによって表示しても良い。 As a diagram showing the frequency distribution displayed on the display unit 24, as shown in FIG. 4, for example, a histogram, a color, a shade of color, a size of a width, a length of a length, a line graph, a curve, an envelope, etc. are displayed. May be.
　なお、図１の予測部２６は、ニューラルネットワーク、ディープラーニング、重回帰計算のうち少なくとも一つを備えるが、学習データを用いて機械学習する計算手法であればこれらのみには限らない。 Note that the prediction unit 26 in FIG. 1 includes at least one of neural network, deep learning, and multiple regression calculation, but is not limited to these as long as it is a calculation method for machine learning using training data.
　以上説明したように、本実施例の業務支援システムは、過去の予測値（過去の時点での予測値３６）と過去の実績値（過去の時点での実績値４５）、或いは過去の予測値と過去の実績値から算出された過去の予測誤差１２を記憶する記憶部（記憶部１０，予測値記憶部４２，実績値記憶部４３）と、過去の予測誤差１２を統計処理し、予測誤差統計処理結果１６を求める統計解析部１４と、統計解析部１４で求めた予測誤差統計処理結果１６を将来の予測値２０に加減算して予測誤差反映予測値２２を求める予測誤差反映予測値計算部１８と、予測誤差反映予測値計算部１８で求めた予測誤差反映予測値２２を表示する表示部２４を備えている。 As described above, the business support system of this embodiment has a past predicted value (predicted value 36 at a past time point) and a past actual value (actual value 45 at a past time point), or a past predicted value. The storage unit (storage unit 10, predicted value storage unit 42, actual value storage unit 43) that stores the past prediction error 12 calculated from the past actual values and the past prediction error 12 are statistically processed, and the prediction error is predicted. Prediction error reflection predicted value calculation unit that obtains the prediction error reflection prediction value 22 by adding / subtracting the prediction error statistical processing result 16 obtained by the statistical analysis unit 14 and the statistical analysis unit 14 that obtains the statistical processing result 16 to the future prediction value 20. 18 and a display unit 24 for displaying the prediction error reflection prediction value 22 obtained by the prediction error reflection prediction value calculation unit 18.
　また、本実施例の業務支援方法は、（ａ）過去の予測値（過去の時点での予測値３６）と過去の実績値（過去の時点での実績値４５）から過去の予測誤差１２を算出するステップと、（ｂ）（ａ）ステップで算出した過去の予測誤差１２を統計処理し、予測誤差統計処理結果１６を求めるステップと、（ｃ）（ｂ）ステップで求めた予測誤差統計処理結果１６を将来の予測値２０に加減算して予測誤差反映予測値２２を求めるステップと、（ｄ）（ｃ）ステップで求めた予測誤差反映予測値２２を表示部２４に表示するステップを有している。 Further, in the business support method of this embodiment, (a) a past prediction error 12 is obtained from a past predicted value (predicted value 36 at a past time point) and a past actual value (actual value 45 at a past time point). The step to calculate, the step to obtain the prediction error statistical processing result 16 by statistically processing the past prediction error 12 calculated in steps (b) and (a), and the prediction error statistical processing obtained in steps (c) and (b). It has a step of adding and subtracting the result 16 to the future predicted value 20 to obtain a predicted error reflecting predicted value 22, and a step of displaying the predicted error reflected predicted value 22 obtained in steps (d) and (c) on the display unit 24. ing.
　これにより、業務計画を策定する管理者は過去の実績に基づく予測部の予測誤差を把握したうえで、将来の予測値がどの程度の値となるかを容易に推測でき、より適切な業務計画の策定に活用できる。 As a result, the manager who formulates the business plan can easily guess how much the future forecast value will be after grasping the prediction error of the forecasting department based on the past performance, and a more appropriate business plan. It can be used for the formulation of.
　さらに、管理者は自身で推測した値を図形の移動で設定することで対話的に将来の業務計画を自動計算できるため、業務計画を短時間に策定することができる。 Furthermore, since the administrator can interactively automatically calculate the future business plan by setting the value estimated by himself by moving the figure, the business plan can be formulated in a short time.
　図５から図８を参照して、本発明の実施例２に係る業務支援システム及び業務支援方法について説明する。 The business support system and the business support method according to the second embodiment of the present invention will be described with reference to FIGS. 5 to 8.
　図５は、本実施例の業務支援システムの全体構成を示す図である。図５に示す本実施例の業務支援システムは、実施例１（図１）で示した構成要素に、図形位置情報５２、業務計画自動計算部５４、業務計画５６、影響を受けるデータ５８が加わっている。その他の構成は、実施例１（図１）と同様である。このうち図形位置情報５２について図６を用いて説明する。 FIG. 5 is a diagram showing the overall configuration of the business support system of this embodiment. In the business support system of this embodiment shown in FIG. 5, the graphic position information 52, the business plan automatic calculation unit 54, the business plan 56, and the affected data 58 are added to the components shown in the first embodiment (FIG. 1). ing. Other configurations are the same as those in the first embodiment (FIG. 1). Of these, the figure position information 52 will be described with reference to FIG.
　図６では、横軸（日数）として1日、2日、3日、4日の箇所に正方形が表示されている。この図形は管理者が推測した貨物数を示しており、マウスやキーボード、タッチパネルなどの操作により、それぞれの日において表示部２４で上下に移動することができる。図形の形状は正方形には限定されず、異なる形状であっても良い。移動した図形の縦軸（貨物数）の値が図形位置情報５２となる。 In Fig. 6, squares are displayed on the horizontal axis (number of days) at the 1st, 2nd, 3rd, and 4th days. This figure shows the number of cargoes estimated by the administrator, and can be moved up and down on the display unit 24 on each day by operating the mouse, keyboard, touch panel, and the like. The shape of the figure is not limited to a square, and may be a different shape. The value of the vertical axis (number of cargoes) of the moved figure becomes the figure position information 52.
　図５に示すように、図形位置情報５２は表示部２４から業務計画自動計算部５４に与えられる。業務計画自動計算部５４では図形位置情報５２に基づいて業務計画５６を計算して表示部２４に与える。また、業務計画５６によって影響を受けるデータ５８も表示部２４に与えられて表示される。 As shown in FIG. 5, the figure position information 52 is given from the display unit 24 to the business plan automatic calculation unit 54. The business plan automatic calculation unit 54 calculates the business plan 56 based on the graphic position information 52 and gives it to the display unit 24. Further, the data 58 affected by the business plan 56 is also given to the display unit 24 and displayed.
　表示部２４に表示される業務計画５６の例を図７に示す。立案する業務計画５６はこの例では稼働させるトラックの運転台数である。図６で示した貨物数に対し、それを配送するトラックが何台必要かを示したものがこの図７である。横軸（日数）として-4日～0日まではトラック運転台数に関する本日を含む過去の実績値が表示されており、横軸（日数）として1日～4日までが将来の業務計画５６に相当する。 FIG. 7 shows an example of the business plan 56 displayed on the display unit 24. The business plan 56 to be drafted is the number of trucks in operation in this example. FIG. 7 shows how many trucks are required to deliver the cargo to the number of cargoes shown in FIG. The horizontal axis (days) is -4 days to 0 days, and the past actual values including today are displayed, and the horizontal axis (days) is 1 to 4 days in the future business plan 56. Equivalent to.
　業務計画５６の計算方法にはさまざまな方法があるが、一つの単純な方法としては図形位置情報５２によって設定された貨物数をトラック1台あたりの輸送能力で除算し、その値を切り上げて整数化する。図６と図７は同一画面内で同時に表示されることが望ましく、図６中の図形（正方形）を上下へ移動させる操作に応じて図７の業務計画５６の表示がリアルタイムで変化することが望ましい。 There are various calculation methods for the business plan 56, but one simple method is to divide the number of cargoes set by the graphic position information 52 by the transport capacity per truck, and round up the value to obtain an integer. To become. It is desirable that FIGS. 6 and 7 are displayed simultaneously on the same screen, and the display of the business plan 56 of FIG. 7 may change in real time according to the operation of moving the figure (square) in FIG. 6 up and down. desirable.
　業務計画を策定する管理者は、貨物数の将来の予測値２０やその予測値に過去の予測誤差の度数分布を加減算した予測誤差反映予測値２２を図６で確認しながら貨物数を推測し、推測数が変わればトラック運転台数が何台になるかを対話的に容易に把握できる。 The manager who formulates the business plan estimates the number of cargoes while checking the future predicted value 20 of the number of cargoes and the predicted error reflection predicted value 22 obtained by adding / subtracting the frequency distribution of the past predicted error to the predicted value in FIG. , It is easy to interactively grasp how many trucks will be operated if the estimated number changes.
　トラックの運転台数が少ないことが望ましい場合、推測数をわずかに小さくすることでトラック運転台数を1台減らすことができるのであれば、推測数を減らした条件での計画を容易に策定できる。逆に、推測数をわずかに大きくすることでトラック運転台数が1台増えるのであれば、リスクを考えて推測数を増やした条件での計画を策定することも可能である。 If it is desirable that the number of trucks in operation is small, and if the number of trucks in operation can be reduced by one by slightly reducing the estimated number, it is possible to easily formulate a plan under the condition that the estimated number is reduced. On the contrary, if the number of trucks operated increases by one by slightly increasing the estimated number, it is possible to formulate a plan under the condition that the estimated number is increased in consideration of the risk.
　自動計算された将来の業務計画により影響を受けるデータ５８の表示例を図８に示す。将来の業務計画により影響を受けるデータ５８として、倉庫内在庫貨物の数の推移が折れ線グラフで表示されている。図７と同様に、横軸（日数）として-4日～0日までは倉庫内在庫貨物の本日を含む過去の実績値が表示されている。横軸（日数）として1日～4日までが将来の業務計画５６により影響を受けるデータ５８である。影響を受けるデータ５８の計算方法にはさまざまな方法があるが、単純な方法としては図６の貨物数から図７のトラック運転台数にトラック1台あたりの輸送能力を乗じた値を減算した結果に、その前時刻までの倉庫内在庫貨物を加算した値として計算できる。 Figure 8 shows an example of displaying the data 58 that is affected by the automatically calculated future business plan. As data 58 affected by future business plans, changes in the number of cargoes in stock in the warehouse are displayed in a line graph. Similar to FIG. 7, the horizontal axis (days) is -4 days to 0 days, and the past actual values including today of the inventories in the warehouse are displayed. The data 58 on the horizontal axis (number of days) from 1 to 4 days is affected by the future business plan 56. There are various methods for calculating the affected data 58, but a simple method is the result of subtracting the value obtained by multiplying the number of trucks in FIG. 7 by the transport capacity per truck from the number of cargoes in FIG. It can be calculated as the value obtained by adding the cargo in stock in the warehouse up to the time before that.
　図８も図６と同一画面内で同時に表示されることが望ましく、図６の図形（正方形）を上下に移動させる操作に応じて図８の影響を受けるデータ５８の表示がリアルタイムで変化することが望ましい。 It is desirable that FIG. 8 is also displayed at the same time on the same screen as FIG. 6, and the display of the data 58 affected by FIG. 8 changes in real time according to the operation of moving the figure (square) of FIG. 6 up and down. Is desirable.
　影響を受けるデータ５８が一項目ではなく複数項目ある場合には、それぞれの項目に関する影響を受けるデータ５８のトレンドグラフも同時に同一画面に表示されることが良い。図８の例では倉庫内在庫貨物が表示されているが、業務計画を策定する管理者は図６中の図形（正方形）を上下に移動させながら倉庫内在庫貨物の数の変化を見ることで、倉庫容量の上限値を念頭においたうえで倉庫内在庫貨物の数が適正な水準となるよう、トラック運転台数の計画を策定できる。 When the affected data 58 has multiple items instead of one item, the trend graph of the affected data 58 for each item should be displayed on the same screen at the same time. In the example of FIG. 8, the warehouse inventory cargo is displayed, but the manager who formulates the business plan can see the change in the number of warehouse inventory cargo while moving the figure (square) in FIG. 6 up and down. With the upper limit of warehouse capacity in mind, it is possible to formulate a plan for the number of trucks in operation so that the number of cargo in stock in the warehouse will be at an appropriate level.
　以上説明したように、本実施例によれば、自動計算した業務計画によって影響されるデータも表示されるため、実施例１の効果に加えて、表示される業務計画と影響されるデータとを確認しながら推測値を調整できる。 As described above, according to this embodiment, the data affected by the automatically calculated business plan is also displayed. Therefore, in addition to the effect of the first embodiment, the displayed business plan and the affected data are displayed. You can adjust the estimated value while checking.
　なお、本発明は上記した実施例に限定されるものではなく、様々な変形例が含まれる。例えば、上記の実施例は本発明に対する理解を助けるために詳細に説明したものであり、必ずしも説明した全ての構成を備えるものに限定されるものではない。また、ある実施例の構成の一部を他の実施例の構成に置き換えることが可能であり、また、ある実施例の構成に他の実施例の構成を加えることも可能である。また、各実施例の構成の一部について、他の構成の追加・削除・置換をすることが可能である。 The present invention is not limited to the above-described embodiment, but includes various modifications. For example, the above embodiments have been described in detail to aid in understanding of the present invention and are not necessarily limited to those comprising all of the described configurations. Further, it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. Further, it is possible to add / delete / replace a part of the configuration of each embodiment with another configuration.
　１０…記憶部、１２…過去の予測誤差、１４…統計解析部、１６…予測誤差統計処理結果、１８…予測誤差反映予測値計算部、２０…将来の予測値、２２…予測誤差反映予測値、２４…表示部、２６…予測部、２８…特定期間設定部、３０…特定期間設定入力値、３２…学習時期記憶部、３４…予測モデルの学習時期、３６…過去の時点での予測値、３８…予測誤差統計処理結果記憶部、４０…過去の時点での予測誤差統計処理結果、４２…予測値記憶部、４３…実績値記憶部、４４…過去時点予測誤差反映予測値計算部、４５…過去の時点での実績値、４６…過去時点予測誤差反映予測値、４８…対象データ時間長設定部、５０…時間長、５２…図形位置情報、５４…業務計画自動計算部、５６…業務計画、５８…影響を受けるデータ 10 ... Storage unit, 12 ... Past prediction error, 14 ... Statistical analysis unit, 16 ... Prediction error statistical processing result, 18 ... Prediction error reflection prediction value calculation unit, 20 ... Future prediction value, 22 ... Prediction error reflection prediction value , 24 ... Display unit, 26 ... Prediction unit, 28 ... Specific period setting unit, 30 ... Specific period setting input value, 32 ... Learning time storage unit, 34 ... Prediction model learning time, 36 ... Predicted value at a past time , 38 ... Prediction error statistical processing result storage unit, 40 ... Prediction error statistical processing result at a past time point, 42 ... Predicted value storage unit, 43 ... Actual value storage unit, 44 ... Past time point prediction error reflection predicted value calculation unit, 45 ... Actual value at the past time point, 46 ... Past time point prediction error reflection prediction value, 48 ... Target data time length setting unit, 50 ... Time length, 52 ... Figure position information, 54 ... Business plan automatic calculation unit, 56 ... Business plan, 58 ... Affected data

Claims (15)

  1. 　業務計画の策定を支援する業務支援システムにおいて、
    　過去の予測値と過去の実績値、或いは過去の予測値と過去の実績値から算出された過去の予測誤差を記憶する記憶部と、
    　過去の予測誤差を統計処理し、予測誤差統計処理結果を求める統計解析部と、
    　前記統計解析部で求めた予測誤差統計処理結果を将来の予測値に加減算して予測誤差反映予測値を求める予測誤差反映予測値計算部と、
    　前記予測誤差反映予測値計算部で求めた予測誤差反映予測値を表示する表示部と、
    　を備えることを特徴とする業務支援システム。     In a business support system that supports the formulation of business plans
    A storage unit that stores past predicted values and past actual values, or past predicted errors calculated from past predicted values and past actual values.
    Statistical analysis unit that statistically processes past prediction errors and obtains prediction error statistical processing results,
    Prediction error reflection predicted value calculation unit that obtains prediction error reflection prediction value by adding / subtracting the prediction error statistical processing result obtained by the statistical analysis unit to the future prediction value.
    A display unit that displays the prediction error reflection prediction value obtained by the prediction error reflection prediction value calculation unit, and a display unit.
    A business support system characterized by being equipped with.
  2. 　請求項１に記載の業務支援システムにおいて、
    　基準時刻より将来の予測値を出力する予測部を備えることを特徴とする業務支援システム。     In the business support system according to claim 1,
    A business support system characterized by having a prediction unit that outputs future prediction values from the reference time.
  3. 　請求項２に記載の業務支援システムにおいて、
    　前記予測部は、ニューラルネットワーク、ディープラーニング、重回帰計算のうち少なくとも１つを備えることを特徴とする業務支援システム。     In the business support system according to claim 2,
    The prediction unit is a business support system including at least one of a neural network, deep learning, and multiple regression calculation.
  4. 　請求項１から３のいずれか１項に記載の業務支援システムにおいて、
    　前記表示部は、一方の軸が時間であり、他方の軸が将来の予測誤差反映予測値であるグラフを表示することを特徴とする業務支援システム。     In the business support system according to any one of claims 1 to 3,
    The display unit is a business support system characterized in that one axis is time and the other axis displays a graph which is a predicted value reflecting a future prediction error.
  5. 　請求項１から４のいずれか１項に記載の業務支援システムにおいて、
    　前記統計解析部で統計処理の対象となる過去の予測誤差の期間を設定可能な特定期間設定部を備えることを特徴とする業務支援システム。     In the business support system according to any one of claims 1 to 4,
    A business support system including a specific period setting unit capable of setting a period of past prediction errors to be subject to statistical processing in the statistical analysis unit.
  6. 　請求項５に記載の業務支援システムにおいて、
    　予測モデルが学習した時期である予測モデル学習時期を記憶する学習時期記憶部を備え、
    　前記学習時期記憶部から出力された予測モデル学習時期が前記特定期間設定部に表示されることを特徴とする業務支援システム。     In the business support system according to claim 5,
    Equipped with a learning time storage unit that stores the prediction model learning time, which is the time when the prediction model learned.
    A business support system characterized in that a predictive model learning time output from the learning time storage unit is displayed in the specific period setting unit.
  7. 　請求項１から６のいずれか１項に記載の業務支援システムにおいて、
    　前記予測誤差統計処理結果が度数分布を示す図であることを特徴とする業務支援システム。     In the business support system according to any one of claims 1 to 6,
    A business support system characterized in that the prediction error statistical processing result is a diagram showing a frequency distribution.
  8. 　請求項７に記載の業務支援システムにおいて、
    　前記度数分布を示す図は、ヒストグラム、色、色の濃淡、幅の大小、長さの長短、折れ線グラフ、曲線、包絡線のうち、少なくともいずれかで示されることを特徴とする業務支援システム。     In the business support system according to claim 7,
    The diagram showing the frequency distribution is a business support system characterized in that it is shown by at least one of a histogram, a color, a shade of color, a size of a width, a length of a length, a line graph, a curve, and an envelope.
  9. 　請求項２に記載の業務支援システムにおいて、
    　前記表示部に、前記予測部から出力された将来の予測値を表示することを特徴とする業務支援システム。     In the business support system according to claim 2,
    A business support system characterized in that a future predicted value output from the predicted unit is displayed on the display unit.
  10. 　請求項１から９のいずれか１項に記載の業務支援システムにおいて、
    　前記記憶部は、過去の時点での予測値を記憶する予測値記憶部と、
    　過去の時点での実績値を記憶する実績値記憶部と、を備え、
    　前記予測値記憶部から出力された過去の時点での予測値と、前記実績値記憶部から出力された過去の時点での実績値とを前記表示部に表示することを特徴とする業務支援システム。     In the business support system according to any one of claims 1 to 9,
    The storage unit includes a predicted value storage unit that stores predicted values at a past time point and a predicted value storage unit.
    Equipped with an actual value storage unit that stores actual values at the past time,
    A business support system characterized in that the predicted value at a past time point output from the predicted value storage unit and the actual value at a past time point output from the actual value storage unit are displayed on the display unit. ..
  11. 　請求項１０に記載の業務支援システムにおいて、
    　前記記憶部は、過去の時点での予測誤差統計処理結果を記憶する予測誤差統計処理結果記憶部と、
    　前記予測誤差統計処理結果記憶部に記憶された過去の時点での予測誤差統計処理結果と、前記予測値記憶部に記憶された過去の時点での予測値を加減算して過去時点予測誤差反映予測値を求める過去時点予測誤差反映予測値計算部と、を備え、
    　前記過去時点予測誤差反映予測値計算部で求めた過去時点予測誤差反映予測値を前記表示部に表示することを特徴とする業務支援システム。     In the business support system according to claim 10,
    The storage unit includes a prediction error statistical processing result storage unit that stores prediction error statistical processing results at a past time point.
    Prediction error Prediction error reflection prediction by adding or subtracting the prediction error statistical processing result stored in the prediction error statistical processing result storage unit at the past time point and the prediction value at the past time point stored in the prediction value storage unit. It is equipped with a forecast value calculation unit that reflects past time forecast errors to obtain values.
    A business support system characterized in that the past time prediction error reflection prediction value obtained by the past time prediction error reflection prediction value calculation unit is displayed on the display unit.
  12. 　請求項１１に記載の業務支援システムにおいて、
    　予測対象の未来の時刻までの時間長が異なる複数の予測値があり、
    　前記過去時点予測誤差反映予測値計算部で用いる対象データの時間長を指定する対象データ時間長設定部を備えることを特徴とする業務支援システム。     In the business support system according to claim 11,
    There are multiple forecast values with different time lengths to the future time of the forecast target,
    A business support system including a target data time length setting unit for designating a time length of target data used in the past time point prediction error reflection prediction value calculation unit.
  13. 　請求項１から１２のいずれか１項に記載の業務支援システムにおいて、
    　前記表示部に上下へ移動可能な図形を重畳表示し、前記図形の位置に応じて将来の業務計画を自動計算する業務計画自動計算部と、
    　前記業務計画自動計算部で自動計算された将来の業務計画を表示する画面と、
    　を備えることを特徴とする業務支援システム。     In the business support system according to any one of claims 1 to 12,
    A business plan automatic calculation unit that superimposes a figure that can be moved up and down on the display unit and automatically calculates a future business plan according to the position of the figure.
    A screen for displaying a future business plan automatically calculated by the business plan automatic calculation unit, and
    A business support system characterized by being equipped with.
  14. 　請求項１３に記載の業務支援システムにおいて、
    　前記業務計画自動計算部で自動計算された将来の業務計画により影響を受けるデータを前記表示部に表示することを特徴とする業務支援システム。     In the business support system according to claim 13,
    A business support system characterized in that data affected by a future business plan automatically calculated by the business plan automatic calculation unit is displayed on the display unit.
  15. 　業務計画の策定を支援する業務支援方法であって、
    　（ａ）過去の予測値と過去の実績値から過去の予測誤差を算出するステップと、
    　（ｂ）前記（ａ）ステップで算出した過去の予測誤差を統計処理し、予測誤差統計処理結果を求めるステップと、
    　（ｃ）前記（ｂ）ステップで求めた予測誤差統計処理結果を将来の予測値に加減算して予測誤差反映予測値を求めるステップと、
    　（ｄ）前記（ｃ）ステップで求めた予測誤差反映予測値を表示部に表示するステップと、
    　を有することを特徴とする業務支援方法。     It is a business support method that supports the formulation of business plans.
    (A) Steps to calculate the past prediction error from the past predicted value and the past actual value,
    (B) A step of statistically processing the past prediction error calculated in the above (a) step to obtain a prediction error statistical processing result, and a step of obtaining the prediction error statistical processing result.
    (C) A step of adding / subtracting the prediction error statistical processing result obtained in the step (b) to the future prediction value to obtain a prediction error reflection predicted value.
    (D) A step of displaying the prediction error reflection predicted value obtained in the step (c) above on the display unit, and
    A business support method characterized by having.
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